{"title":"计算预测量的有效方法","authors":"A. Rakitskiy","doi":"10.1109/SIBIRCON.2017.8109859","DOIUrl":null,"url":null,"abstract":"The prediction of time series is one of the most important scientific fields in information sciences (for example, in the development of AI). One of the most difficult tasks is how to reduce the time of building a prediction. In this paper the efficient method of calculating universal-coding-based predictors is presented. This approach allows to calculate the Krichevsky predictor and similar ones with linear complexity.","PeriodicalId":135870,"journal":{"name":"2017 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"The efficient approach of calculating the predictors\",\"authors\":\"A. Rakitskiy\",\"doi\":\"10.1109/SIBIRCON.2017.8109859\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The prediction of time series is one of the most important scientific fields in information sciences (for example, in the development of AI). One of the most difficult tasks is how to reduce the time of building a prediction. In this paper the efficient method of calculating universal-coding-based predictors is presented. This approach allows to calculate the Krichevsky predictor and similar ones with linear complexity.\",\"PeriodicalId\":135870,\"journal\":{\"name\":\"2017 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIBIRCON.2017.8109859\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIBIRCON.2017.8109859","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The efficient approach of calculating the predictors
The prediction of time series is one of the most important scientific fields in information sciences (for example, in the development of AI). One of the most difficult tasks is how to reduce the time of building a prediction. In this paper the efficient method of calculating universal-coding-based predictors is presented. This approach allows to calculate the Krichevsky predictor and similar ones with linear complexity.